Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework
Year: 2021
Type: conference-paper
Abstract: Recent research has demonstrated how racial biases against users who write African American English exists in popular toxic language datasets. While previous work has focused on a single fairness criteria, we propose to use additional descriptive fairness metrics to better understand the source of these biases. We demonstrate that different benchmark classifiers, as well as two in-process bias-remediation techniques, propagate racial biases even in a larger corpus. We then propose a novel ensemble-framework that uses a specialized classifier that is fine-tuned to the African American English dialect. We show that our proposed framework substantially reduces the racial biases that the model learns from these datasets. We demonstrate how the ensemble framework improves fairness metrics across all sample datasets with minimal impact on the classification performance, and provide empirical evidence for its ability to unlearn the annotation biases towards authors who use African American English.
Authors: Matan Halevy, Camille Harris, Amy Bruckman, Diyi Yang, Ayanna Howard
Institutions: Georgia Institute of Technology, The Ohio State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
Funder: Cisco Systems
Award: G4155337759
OpenAlex ID: W3202038256